AI IN PSYCHIATRIC DIAGNOSTICS – A REVIEW
DOI:
https://doi.org/10.31435/ijitss.3(51).2026.6494Keywords:
Artificial Intelligence, Psychiatry, Diagnostics, Material Language Transmission, Neuroimaging, EthicsAbstract
Introduction: Artificial intelligence (AI) is increasingly being used in psychiatry, with side effects on solutions stemming from the subjectivity of diagnosis, limited care, and biological complexity, which is subject to threats. Mental disorders affect 293 million people worldwide and pose a burden on human health [9].
Aim: The aim of this review is to summarize the current state of knowledge on AI applications in psychiatric diagnostics, with specific focus on: (1) analysis of communication traffic of AI algorithms, (2) analysis of the results of AI-based primary control, (3) extension of methodological and ethical implications, and (4) extension of research.
Methods: A review of the research literature was conducted in the field of Basic Language Processing (NLP) in digital phenotyping, AI-assisted neuroimaging, and the ethical and legal implications of implementing these technologies. Meta-analyses, specific reviews, and original empirical studies completed between 2015 and 2026 were analyzed.
Results: A meta-analysis reported a cumulative AI diagnostic accuracy of 85% and a therapeutic efficacy of 84% in specific applications [8]. NLP enabled independent assessment, achieving an 86% (AUC 0.93) in studies on psychosis risk states [18]. Chatbots (Woebot, Wysa, Youper) demonstrate the consequences of problem occurrence and anxiety [9]. A review of 555 neuroimaging models revealed that 83.1% of the symptoms appear as a consequence rather than being triggered by a utility [32]. The most important ethical concerns were identified, including algorithm opacity ("black box"), liability, and data privacy [54, 56, 61].
Conclusions: AI in psychiatric diagnostics has demonstrated transformative potential, particularly in the areas of NLP and digital phenotyping, but current neuroimaging models require methodological improvements. The development of comprehensive ethical frameworks and extensions, simple algorithms, and model validation in large, population-based cohorts are essential. The ultimate success of AI in psychiatry will depend on striking a balance between technological innovation and respect for fundamental ethical values, while maintaining a paramount clinical role in diagnostic and therapeutic procedures.
References
O'Connell, K., et al. (2025). Genomics yields biological and phenotypic insights into bipolar disorder. Nature, 639, 968–975. https://doi.org/10.1038/s41586-024-08468-9
Sun, C., McEwan, A., Boulton, K. A., Demetriou, E. A., et al. (2025). Artificial intelligence for tracking social behaviours and supporting an autism spectrum disorder diagnosis: Systematic review and meta-analysis. eBioMedicine, 120, 105931. https://doi.org/10.1016/j.ebiom.2025.105931
Huggins, A. A., Baird, C. L., Briggs, M., Laskowitz, S., Hussain, A., Fouda, S., et al. (2024). Smaller total and subregional cerebellar volumes in posttraumatic stress disorder: A mega-analysis by the ENIGMA-PGC PTSD workgroup. Molecular Psychiatry, 29, 611–623. https://doi.org/10.1038/s41380-023-02347-9
Saboori Amleshi, R., Ilaghi, M., Rezaei, M., et al. (2025). Predictive utility of artificial intelligence on schizophrenia treatment outcomes: A systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews, 170, 105968. https://doi.org/10.1016/j.neubiorev.2024.105968
Pham, K. T., Nabizadeh, A., & Selek, S. (2022). Artificial intelligence and chatbots in psychiatry. Psychiatric Quarterly, 93, 249–253. https://doi.org/10.1007/s11126-022-09973-8
Kolding, S., Lundin, R. M., Hansen, L., & Østergaard, S. D. (2025). Use of generative artificial intelligence (AI) in psychiatry and mental health care: A systematic review. Acta Neuropsychiatrica, 37, e37. https://doi.org/10.1017/neu.2024.50
Dehbozorgi, R., Zangeneh, S., Khooshab, E., Hafezi Nia, D., et al. (2025). The application of artificial intelligence in the field of mental health: A systematic review. BMC Psychiatry, 25, 132. https://doi.org/10.1186/s12888-025-06842-9
Rony, M. K. K., Das, D. C., Khatun, M. T., Ferdousi, S., Akter, M., et al. (2025). Artificial intelligence in psychiatry: A systematic review and meta-analysis of diagnostic and therapeutic efficacy. Digital Health, 11, 20552076251330528. https://doi.org/10.1177/20552076251330528
Farzan, M., Ebrahimi, H., Pourali, M., & Sabeti, F. (2025). Artificial intelligence-powered cognitive behavioral therapy chatbots: A systematic review. Iranian Journal of Psychiatry, 20(1), 102–110. https://doi.org/10.18502/ijps.v20i1.17395
Au Yeung, J., Shek, A., Searle, T., et al. (2024). Natural language processing data services for healthcare providers. BMC Medical Informatics and Decision Making, 24(1), 356. https://doi.org/10.1186/s12911-024-02784-w
Le Glaz, A., Haralambous, Y., Kim-Dufor, D. H., et al. (2021). Machine learning and natural language processing in mental health: Systematic review. Journal of Medical Internet Research, 23(5), e15708. https://doi.org/10.2196/15708
Bedi, G., Carrillo, F., et al. (2015). Automated analysis of free speech predicts psychosis onset in high-risk youths. NPJ Schizophrenia, 1(1), 1–7. https://www.nature.com/articles/npjschz201530
Guo, Z., Lai, A., Thygesen, J. H., Farrington, J., Keen, T., & Li, K. (2024). Large language models for mental health applications: Systematic review. JMIR Mental Health, 11, e57400. https://doi.org/10.2196/57400
Névéol, A., Dalianis, H., Velupillai, S., Savova, G., & Zweigenbaum, P. (2018). Clinical natural language processing in languages other than English: Opportunities and challenges. Journal of Biomedical Semantics, 9(1), 12. https://doi.org/10.1186/s13326-018-0179-8
Rabbani, N., Bedgood, M., Brown, C., et al. (2023). A natural language processing model to identify confidential content in adolescent clinical notes. Applied Clinical Informatics, 14(3), 400–407. https://doi.org/10.1055/a-2051-9764
Funk, B., Sadeh-Sharvit, S., Fitzsimmons-Craft, E. E., et al. (2020). A framework for applying natural language processing in digital health interventions. Journal of Medical Internet Research, 22(2), e13855. https://doi.org/10.2196/13855
Trayvick, J., Barkley, S. B., McGowan, A., et al. (2024). Speech and language patterns in autism: Towards natural language processing as a research and clinical tool. Psychiatry Research, 340, 116109. https://doi.org/10.1016/j.psychres.2024.116109
Argolo, F., Ramos, W. H. P., Mota, N. B., et al. (2024). Natural language processing in at-risk mental states: Enhancing the assessment of thought disorders and psychotic traits with semantic dynamics and graph theory. Brazilian Journal of Psychiatry, 46, e20233419. https://doi.org/10.47626/1516-4446-2023-3419
Deneault, A., Dumais, A., Désilets, M., & Hudon, A. (2024). Natural language processing and schizophrenia: A scoping review of uses and challenges. Journal of Personalized Medicine, 14(7), 744. https://doi.org/10.3390/jpm14070744
Tonon, A. C., Nexha, A., da Silva, M. M., Gomes, F. A., et al. (2024). Sleep and circadian disruption in bipolar disorders: From psychopathology to digital phenotyping in clinical practice. Psychiatry and Clinical Neurosciences. https://doi.org/10.1111/pcn.13729
Constant, A., Koksal, C. E., & Palaniyappan, L. (2026). Setting digital psychiatry in motion: Towards dynamic digital markers for digital phenotyping. NPP—Digital Psychiatry and Neuroscience, 4(1), 6. https://doi.org/10.1038/s44277-026-00059-y
Kamath, J., Leon, B. R., Jain, N., Keisari, E., & Wang, B. (2022). Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives. World Journal of Psychiatry, 12(3), 393–409. https://doi.org/10.5498/wjp.v12.i3.393
Slack, S. K., & Barclay, L. (2023). First-person disavowals of digital phenotyping and epistemic injustice in psychiatry. Medicine, Health Care and Philosophy, 26, 605–614. https://doi.org/10.1007/s11019-023-10174-8
Oudin, A., Maatoug, R., Bourla, A., Ferreri, F., Bonnot, O., Millet, B., Schoeller, F., Mouchabac, S., & Adrien, V. (2023). Digital phenotyping: Data-driven psychiatry to redefine mental health. Journal of Medical Internet Research, 25, e44502. https://doi.org/10.2196/44502
Shen, F. X., Baum, M. L., Martinez-Martin, N., Miner, A. S., Abraham, M., Brownstein, C. A., Cortez, N., Evans, B. J., Germine, L. T., Glahn, D. C., Grady, C., Holm, I. A., Hurley, E. A., Kimble, S., Lázaro-Muñoz, G., Leary, K., Marks, M., Monette, P. J., Onnela, J. P., . . . Silverman, B. C. (2024). Returning individual research results from digital phenotyping in psychiatry. The American Journal of Bioethics, 24(2), 69–90. https://doi.org/10.1080/15265161.2023.2180109
Sterling, W. A., Sobolev, M., Van Meter, A., Guinart, D., Birnbaum, M. L., Rubio, J. M., & Kane, J. M. (2022). Digital technology in psychiatry: Survey study of clinicians. JMIR Formative Research, 6(11), e33676. https://doi.org/10.2196/33676
Orsolini, L., Fiorani, M., & Volpe, U. (2020). Digital phenotyping in bipolar disorder: Which integration with clinical endophenotypes and biomarkers? International Journal of Molecular Sciences, 21(20), 7684. https://doi.org/10.3390/ijms21207684
Washington, P., Park, N., Srivastava, P., et al. (2020). Data-driven diagnostics and the potential of mobile artificial intelligence for digital therapeutic phenotyping in computational psychiatry. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 5(8), 759–769. https://doi.org/10.1016/j.bpsc.2019.11.015
Liu, J. J., Borsari, B., Li, Y., et al. (2025). Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations. Cell, 188(2), 515–529.e15. https://doi.org/10.1016/j.cell.2024.11.012
Raugh, I. M., James, S. H., Gonzalez, C. M., & Chapman, H. C. (2021). Digital phenotyping adherence, feasibility, and tolerability in outpatients with schizophrenia. Journal of Psychiatric Research, 138, 436–443. https://doi.org/10.1016/j.jpsychires.2021.04.022
Mouchabac, S., Conejero, I., Lakhlifi, C., Msellek, I., Malandain, L., Adrien, V., . . . Maatoug, R. (2021). Usprawnianie procesu podejmowania decyzji klinicznych w psychiatrii: Wdrożenie fenotypowania cyfrowego może złagodzić wpływ indywidualnych uprzedzeń poznawczych pacjentów i lekarzy. Dialogues in Clinical Neuroscience, 23(1), 52–61. https://doi.org/10.1080/19585969.2022.2042165
Chen, Z., Liu, X., Yang, Q., Wang, Y., Miao, K., Gong, Z., Yu, Y., Leonov, A., Liu, C., Feng, Z., & Hu, C.-P. (2023). Evaluation of risk of bias in neuroimaging-based artificial intelligence models for psychiatric diagnosis: A systematic review. JAMA Network Open, 6(3), e231671. https://doi.org/10.1001/jamanetworkopen.2023.1671
Marek, S., & Laumann, T. O. (2024). Replicability and generalizability in population psychiatric neuroimaging. Neuropsychopharmacology, 50(1), 52–57. https://doi.org/10.1038/s41386-024-01960-w
Vogel, A. C., & Black, K. J. (2024). Brain imaging in routine psychiatric practice. Missouri Medicine, 121(1), 37–43. https://pmc.ncbi.nlm.nih.gov/articles/PMC10887461/
Cope, T. E., Weil, R. S., Düzel, E., Dickerson, B. C., & Rowe, J. B. (2020). Advances in neuroimaging to support translational medicine in dementia. Journal of Neurology, Neurosurgery & Psychiatry, 91(8), 882–894. https://doi.org/10.1136/jnnp-2019-322402
Ersoy, S., Ersoy, E. H., Danis, A., & Turkoglu, S. A. (2025). Trends and global productivity in artificial intelligence research in clinical neurology and neuroimaging: A bibliometric analysis from 1980 to 2024. Cerebral Cortex, 35(6), bhaf148. https://doi.org/10.1093/cercor/bhaf148
Seriramulu, V. P., Suppiah, S., & Lee, H. H. (2024). Review of MR spectroscopy analysis and artificial intelligence applications. Medical Journal of Malaysia, 79(1), 103–109. https://www.e-mjm.org/2024/v79n1/alzheimers-disease.pdf
Tejavibulya, L., Rolison, M., Gao, S., Liang, Q., Peterson, H., Dadashkarimi, J., Farruggia, M. C., Hahn, C. A., Noble, S., Lichenstein, S. D., Pollatou, A., Dufford, A. J., & Scheinost, D. (2022). Predicting the future of neuroimaging predictive models in mental health. Molecular Psychiatry, 27(8), 3129–3137. https://doi.org/10.1038/s41380-022-01635-2
Kotochinsky, M., Fonseca, P. E. O., Lopera, V. R., Mora, L., Amador, W. F. O., Sirena, E. C. T., Guimarães, F. B. M., Herlyn, D. L., Sherpa, N. N., Lezana, A. G., & Fagundes, T. P. (2025). Comparative diagnostic performance of artificial intelligence models in structural MRI for schizophrenia: A systematic review and meta-analysis. Asian Journal of Psychiatry, 107, 104759. https://doi.org/10.1016/j.ajp.2025.104759
Zhou, E., Wang, W., Ma, S., Xie, X., Kang, L., Xu, S., Deng, Z., Gong, Q., Nie, Z., Yao, L., Bu, L., Wang, F., & Liu, Z. (2023). Prediction of anxious depression using multimodal neuroimaging and machine learning. NeuroImage, 285, 120499. https://doi.org/10.1016/j.neuroimage.2023.120499
Leenings, R., Winter, N. R., Dannlowski, U., & Hahn, T. (2022). Recommendations for machine learning benchmarks in neuroimaging. NeuroImage, 257, 119298. https://doi.org/10.1016/j.neuroimage.2022.119298
Monaco, F., Vignapiano, A., Di Gruttola, B., Landi, S., et al. (2025). Neuroimaging and machine learning in eating disorders: A systematic review. Eating and Weight Disorders—Studies on Anorexia, Bulimia and Obesity, 30, 46. https://doi.org/10.1007/s40519-025-01720-1
Grosenick, L., & Liston, C. (2026). Multimodal representation learning for parsing biological heterogeneity in psychiatric neuroimaging. Biological Psychiatry, 99(10), 862–873. https://www.biologicalpsychiatryjournal.com/article/S0006-3223(26)00086-7/abstract
Wen, J., Skampardoni, I., Tian, Y. E., Yang, Z., Cui, Y., Erus, G., Hwang, G., Varol, E., Boquet-Pujadas, A., Chand, G. B., Nasrallah, I. M., Satterthwaite, T. D., Shou, H., Shen, L., Toga, A. W., Zalesky, A., & Davatzikos, C. (2025). Neuroimaging endophenotypes reveal underlying mechanisms and genetic factors contributing to progression and development of four brain disorders. Nature Biomedical Engineering, 9, 1920–1937. https://doi.org/10.1038/s41551-025-01310-7
Kam, H., & Jeong, H. (2020). Pharmacogenomic biomarkers and their applications in psychiatry. Genes, 11(12), 1445. https://doi.org/10.3390/genes11121445
Kraguljac, N. V., McDonald, W. M., Widge, A. S., Rodriguez, C. I., Tohen, M., & Nemeroff, C. B. (2021). Neuroimaging biomarkers in schizophrenia. American Journal of Psychiatry, 178(6), 509–521. https://doi.org/10.1176/appi.ajp.2020.20030340
Golub, A., Ordak, M., Nasierowski, T., & Bujalska-Zadrozny, M. (2023). Advanced biomarkers of hepatotoxicity in psychiatry: A narrative review and recommendations for new psychoactive substances. International Journal of Molecular Sciences, 24(11), 9413. https://doi.org/10.3390/ijms24119413
Parellada, M., Andreu-Bernabeu, Á., Burdeus, M., San José Cáceres, A., Urbiola, E., Carpenter, L. L., Kraguljac, N. V., McDonald, W. M., Nemeroff, C. B., Rodriguez, C. I., Widge, A. S., & Sanders, S. J. (2023). In search of biomarkers to guide interventions in autism spectrum disorder: A systematic review. American Journal of Psychiatry, 180(1), 50–61. https://doi.org/10.1176/appi.ajp.21100992
Kraus, B., Zinbarg, R., Braga, R. M., Nusslock, R., Mittal, V. A., & Gratton, C. (2023). Insights from personalized models of brain and behavior for identifying biomarkers in psychiatry. Neuroscience & Biobehavioral Reviews, 152, 105259. https://doi.org/10.1016/j.neubiorev.2023.105259
Morrin, H., & Nour, M. M. (2026). The promise of artificial intelligence–powered speech biomarkers in psychiatry. JAMA Network Open, 9(6), e2620251. https://doi.org/10.1001/jamanetworkopen.2026.20251
Lin, E., Lin, C.-H., & Lane, H.-Y. (2020). Precision psychiatry applications with pharmacogenomics: Artificial intelligence and machine learning approaches. International Journal of Molecular Sciences, 21(3), 969. https://doi.org/10.3390/ijms21030969
Winchester, L. M., Harshfield, E. L., Shi, L., Badhwar, A., Al Khleifat, A., Clarke, N., Dehsarvi, A., Lengyel, I., Lourida, I., Madan, C. R., Marzi, S. J., Proitsi, P., Rajkumar, A. P., Rittman, T., Silajdžić, E., Tamburin, S., Ranson, J. M., & Llewellyn, D. J. (2023). Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia. Alzheimer's & Dementia, 19(12), 5860–5871. https://doi.org/10.1002/alz.13390
Baydili, İ., Tasci, B., & Tasci, G. (2025). Artificial intelligence in psychiatry: A review of biological and behavioral data analyses. Diagnostics, 15(4), 434. https://doi.org/10.3390/diagnostics15040434
Khalique, N., Nasir, M., Ahmed, S., & Siddiqui, K. (2026). The role of artificial intelligence in improving the public health care delivery system in India: A legal-ethical audit. Indian Journal of Community Medicine, 51(1), 32–41. https://doi.org/10.4103/ijcm.ijcm_7_25
Furlan, E., Zamperetti, N., Piccinni, M., Marra, A., Cotogni, P., & Giannini, A. (2026). The Ethics Committee of the Italian Society of Anesthesia, Analgesia, Resuscitation and Intensive Care (SIAARTI)—Artificial intelligence in end-of-life decision-making processes: Ethical reflections. Journal of Anesthesia, Analgesia and Critical Care, 6, Article 72. https://doi.org/10.1186/s44158-026-00215-6
Chau, M. T., Spuur, K. M., White, S., Pyper, A., & Crossman, M. (2026). Malpractice in the machine age: Legal and ethical responses to machine learning in medical imaging. Radiography, 32(3). https://www.radiographyonline.com/article/S1078-8174(26)00015-5/fulltext
Ratkevičiūtė, K., & Aliukonis, V. (2025). Exploring opportunities and challenges of AI in primary healthcare: A qualitative study with family doctors in Lithuania. Healthcare, 13(12), 1429. https://doi.org/10.3390/healthcare13121429
Cohen, I. G., & Slottje, A. (2025). Artificial intelligence and the law of informed consent. In Research handbook on health, AI and the law. Edward Elgar Publishing. https://www.ncbi.nlm.nih.gov/books/NBK613199/
Ueda, D., Kakinuma, T., Fujita, S., Kamagata, K., Fushimi, Y., Ito, R., Matsui, Y., Nozaki, T., Nakaura, T., Fujima, N., Tatsugami, F., Yanagawa, M., Hirata, K., Yamada, A., Tsuboyama, T., Kawamura, M., Fujioka, T., & Naganawa, S. (2024). Fairness of artificial intelligence in healthcare: Review and recommendations. Japanese Journal of Radiology, 42, 3–15. https://doi.org/10.1007/s11604-023-01547-x
Khan, M. M., Shah, N., Shaikh, N., Thabet, A., Alrabayah, T., & Belkhair, S. (2025). Towards secure and trusted AI in healthcare: A systematic review of emerging innovations and ethical challenges. International Journal of Medical Informatics, 195, 105780. https://doi.org/10.1016/j.ijmedinf.2024.105780
Rahsepar Meadi, M., Sillekens, T., Metselaar, S., van Balkom, A., Bernstein, J., & Batelaan, N. (2025). Exploring the ethical challenges of conversational AI in mental health care: Scoping review. JMIR Mental Health, 12, e60432. https://doi.org/10.2196/60432
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Copyright (c) 2026 Wiktor Rybicki, Radosław Dutczak, Aleksandra Sobieska, Patrycja Mularczyk, Julia Jaguszewska, Weronika Martyna Górska, Mateusz Okoń, Alicja Gawlik, Alicja Korpacka, Iga Maria Owczarek-Danowska, Aleksandra Wojtera
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